International Journal of Communication Systems最新文献

This paper presents a compact dual-broadband pattern diversity antenna designed for 5G and Wi-Fi applications. The proposed design combines a pair of short and long monopole structures with a radiating patch to achieve omnidirectional radiation in the lower band (2.53–3.31 GHz) and bidirectional radiation in the upper band (5.49–6.06 GHz). By leveraging mode superposition, the antenna maintains stable gain across both frequency ranges, with measured realized gains of 2.29 and 2.95 dBi, respectively. The structure features wide impedance bandwidths of 26.7% and 9.9%, while maintaining a low-profile form factor of 0.013λ_L. Experimental results align well with simulations, confirming the antenna's effectiveness for multi-system coverage. Due to its compact size and enhanced performance, this antenna is well suited for wireless communication systems.

Cyclic interleaving serves as an effective technique to counteract channel fading. Recently, a successive interference cancellation (SIC) method has been introduced for cyclic interleaved frequency division multiplexing (CIFDM). Although CIFDM-SIC successfully eliminates a major portion of the multipath interference, a significant challenge persists due to the semiorthogonality of the codes, resulting in residual interference. Furthermore, the SIC amplifies noise, resulting in performance degradation. In light of these issues, we propose a novel minimum mean square error-SIC (MMSE-SIC) method for CIFDM. This method is meticulously designed to eliminate residues at each stage of the SIC while concurrently minimizing noise enhancement. The proposed CIFDM-MMSE-SIC demonstrates superior bit error rate performance compared with the current CIFDM-SIC across various realistic scenarios.

We present a simple estimation technique for carrier frequency offset (CFO) and uplink channel in multiuser orthogonal frequency division multiplexing access (OFDMA) systems with reconfigurable intelligent surface (RIS) assistance. Based on the block-impulsive pilot feature, our approach considers a correlation-based method for conducting CFO estimation on the received signal derived from cyclic prefix data, providing robustness against multiuser interference and channel variation. Channel estimation is formulated utilizing all the CFO-compensated signals, which requires only a few number of complex multiplication operations. Compared to the existing approach of the reference, the proposed ameliorated technique offers enhanced estimation performance for our scheme, which is validated through a theoretical analysis of the mean squared error of the proposed estimation. Computer simulations have been conducted to confirm its superior performance and reliability.

With the progress of technology in the field of wireless communication, wireless body area networks (WBANs) have been introduced. WBANs consist of a number of intelligent biomedical sensor nodes and a special node called sink. Sensors are placed on the patient's body or under the skin, and their task is to check physiological parameters and report to the sink. The resources of these nodes are limited, and communication is subject to various issues. Maintaining the quality of communication due to the high importance of the transmitted data and severe limitations is one of the important issues in WBANs. The important challenge in this area is designing a framework for next-hop node selection with covering the quality needs of the transmitted data. This paper proposes a Priority-Based QoS Aware Routing Protocol for WBANs (PQARP). PQARP is a three-step method. In the first step, the sensors are configured. In the second step, data is separated and marked. In the third step, the proposed PQARP routing is done and the most appropriate intermediate node is selected to send data. The simulation results using NS-2 showed the remarkable capabilities of PQARP in covering quality requirements of different data sent and improving the metrics of end-to-end delay, successful delivery ratio, throughput and energy consumption compared to other similar methods.

The rise in demand for higher data rates and more subscribers is fueling mobile data traffic. The limited availability of channel access for data transmission requires user equipment (UE) to wait, which subsequently compromises its energy efficiency. To mitigate power consumption and improve quality of service (QoS) in UE, the discontinuous reception (DRX) mechanism over fifth generation (5G) new radio unlicensed (NR-U) offers a viable solution. Our current work explores this by leveraging DRX within NR-U for standalone (SA) and non-standalone (NSA) deployment modes. We have modeled our approach by introducing a traffic-based analyzer state within our system, which is mathematically investigated by subjecting the analyzed traffic to machine learning (ML)-based beamforming techniques. Furthermore, Q-learning-based reinforcement learning (RL) is implemented in this model to determine optimal DRX strategies under dynamic and stochastic traffic conditions, effectively balancing energy efficiency, delay, and QoS requirements.

Wireless sensor networks (WSNs) face critical energy constraints that directly affect network longevity, especially when scaled to large deployments. This paper uses a multi-stage process to introduce an energy-efficient data collection framework using mobile data collectors (MDCs). We employ a modified weighted set cover approach to identify a minimal set of strategically placed anchor points (APs), significantly reducing the number of required MDCs. After refining them with an optimized fuzzy C-means clustering and a traffic load balancing technique, these APs ensure coverage for all areas and balance the load among each other. Using Euler graph analysis, the routing paths are determined through the minimum spanning tree and perfect matching approaches. Experimental results demonstrate that our suggested method reduces the amount of MDCs by 28%, the distance traveled by tours by 35%, the average delay in data collection by 10%, and increases data transmission efficiency by 16%. The reported results prove that our suggested method helps gather more data energy efficiently and prolong the lifetime of WSN nodes.

A crucial area of research in behavior analysis, computer communication, and widespread technology is human activity recognition (HAR). Deep learning (DL) approaches are effectively deployed to forecast human behaviors with periodic information from mobile phones as well as wearable sensors. DL-based methods are very good at identifying activities, but struggle with time series data. In this research work, wearable sensor network-driven human activity recognition via Philippine eagle optimized statistics-infused neural network (WSN-HAR-PEO-SINN) is suggested. First, the input information is gathered from the Wireless Sensor Data Mining (WISDM) dataset. In preprocessing, the flat files are combined to form a data frame using confidence partitioning sampling filtering (CPSF). After preprocessing, the data is segmented using unpaired multiview graph clustering (UMGC). It is used to segment the number of human activities. The divided output is sent to the feature extraction system using quadratic phase S-transform (QPST), which is utilized to gather local characteristics such as skewness, mean, and deviation from the mean, energy, mel-frequency cepstral coefficients, and entropy. Then, the extracted characteristics are entered into a neural connection termed statistics-informed neural network (SINN) to recognize the human activity with wearable sensor data and categorize the data as ambulation-oriented, hand-oriented activity general, and hand-oriented activity eating. Generally speaking, SINN does not incorporate any optimization technique adaptations for identifying the optimal parameters that guarantee precise identification of human activities. To enhance the weight parameter of the SINN approach, which accurately detects human activity using wearable sensor data, Philippine eagle optimization (PEO) is suggested. The accuracy, precision, recall, F1 score, ROC, computing time, and error rate are among the performance metrics used to assess the effectiveness of the suggested SINN-HAR-WSD-PEO strategy. In contrast to previous approaches, such as CNN-GRU-HAR-WSD, which uses wearable sensor data for deep learning-based human activity recognition (HAR), and CNN-BILSTM-HAR-WSD, which uses a multiple branch CNN-BiLSTM model for employing wearable sensor data for human activity recognition, the suggested SINN-HAR-WSD-PEO method achieves higher accuracy, recall, and precision, which were 22.36%, 25.42%, and 18.27%, 21.36%, 26.42%, and 18.27%, and a multi-task deep learning approach for sensor-based human activity recognition and segmentation (MDNN-SB-HAR) was 21.36%, 22.42%, and 19.27%, respectively.

The growing scale of Internet of Things (IoT) networks poses critical challenges for delay-sensitive communication under strict Quality of Service (QoS) requirements. This paper presents MOCHA, a Mixed-Integer Linear Programming (MILP)-based optimization framework that jointly addresses transmission scheduling and dynamic channel allocation. A tailored Branch-and-Bound algorithm is developed to efficiently explore feasible solutions while ensuring capacity, exclusivity, and deadline constraints. Simulation results demonstrate that MOCHA achieves up to 30% lower transmission delay than leading approaches (MPSORP, MO-DRL, PQTBA). These results highlight the practicality of exact optimization in enabling reliable and delay-aware IoT services such as smart healthcare and autonomous systems.

In the future, massive growth in mobile users in various applications is anticipated. To cope with this growth, the enhanced radio access technology (eRAT) can provide a solution to improve diversity gain and spectral efficiency. The state-of-the-art generalized frequency division multiplexing (GFDM) modulation techniques are combined with a multi-input multi-output (MIMO) antenna system to achieve better resource allocation, out-of-band emission, and signal strengths over the existing orthogonal multiple access (OMA) techniques. In the proposed research paper, the subcarrier index modulation (SIM) and constellation precoding (CP) techniques are combined with the MIMO-GFDM system to improve the performance of diversity gain, spectral efficiency, and energy efficiency. The system is called the SIM-CP-MIMO-GFDM. In the SIM-CP-MIMO-GFDM system, first, a few subcarriers are activated by index modulation bits in quadrature/in-phase dimensions, and then data symbols are constellation precoded. At the receiving end, the data blocks are detected using QR decomposition-based maximum likelihood (ML) detection. Finally, the paper incorporates theoretical and simulation result analysis and compares the proposed SIM-CP-MIMO-GFDM system with existing systems. The proposed SIM-CP-MIMO-GFDM outperforms the existing systems.

Wireless Multimedia Sensor Networks (WMSNs) are plagued with issues of battery life limitation and congestion, which lead to packet loss, energy consumption, and delay. In this paper, an energy-aware congestion control architecture named OCNN-TDO-WMSN, combining Orthogonal Convolutional Neural Networks (OCNN) and Tasmanian Devil Optimization (TDO), is proposed. The system uses a rate-based congestion control with cluster routing to reduce energy and delay. Double Fuzzy Clustering-driven Context Neural Networks (DFCCNN) are employed for clustering, OCNN for cluster head election, and TDO for adaptive packet rate adaptation. Throughput is additionally enhanced through the use of a Semi-Decentralized Energy Routing Algorithm (SDERA). The proposed method is simulated in NS-3 and is tested with parameters of energy consumption, lifetime, delay, throughput, packet delivery ratio (PDR), and reliability. Results indicate that OCNN-TDO-WMSN reaches a throughput of 50 Kbps and a PDR of 95%, better than the current methods.